Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium

2019/12/30 by Bart Cockx, Michael Lechner, Cockx, Bart +3 · 2 citations
Economics, Econometrics and Finance · #Labor market dynamics and wage inequality #Italy: Economic History and Contemporary Issues

paper · pdf · doi:10.48550/arxiv.1912.12864

Abstract

Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that 'black-box' rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20 percent more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70 percent of this gain.

Cited by

• Fairness-Aware and Interpretable Policy Learning
• Labor Policies and Immigrant Employment

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